Induction Motor (IM) faults, such as air gap eccentricity, rotor faults, short-circuits, and bearing
faults, can be detected through conditional monitoring, which requires expensive tools. Digital
Signal Processing (DSP) offers a cost-effective solution by using IM Current Signature
Analysis (IMCSA) to identify faults. Faulty motors exhibit different frequency spectra in their
line current compared to healthy ones due to harmonic components. IMCSA detects and
analyzes these changes using Lab VIEW software for direct online monitoring. This paper
discusses the impact of motor faults, fault tracking, and the use of Fast Fourier Transforms
(FFT) for frequency spectrum analysis from analog current data, outlining the transformation
process and experimental procedures for fault detection.